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Aerodynamic characteristics of a free-flight scramjet vehicle in shock tunnel

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TLDR
In this paper, a multi-component aerodynamic test for an airframe-engine integrated scramjet vehicle model was conducted in the free-piston shock tunnel HIEST, where a new method using multiple piezoelectric accelerometers was developed based on overdetermined system analysis.
Abstract
A multi-component aerodynamic test for an airframe-engine integrated scramjet vehicle model was conducted in the free-piston shock tunnel HIEST. A free-flight force measurement technique was applied to the scramjet vehicle model named MoDKI. A new method using multiple piezoelectric accelerometers was developed based on overdetermined system analysis. Its unique features are the following: (1) The accelerometer’s mounting location can be more flexible. (2) The measurement precision is predicted to be improved by increasing the number of accelerometers. (3) The angular acceleration can be obtained with single-axis translational accelerometers instead of gyroscopes. (4) Through the averaging process of the multiple accelerometers, model natural vibration is expected to be mitigated. With eight model-onboard single-axis accelerometers, the three-component aerodynamic coefficients (Drag, Lift, and Pitching moment) of MoDKI were successfully measured at the angle of attack from 0.7 to 3.4 degrees under a Mach 8 free-stream test flow condition. A linear regression fitting revealed a 95% prediction interval as the measurement precision of each aerodynamic coefficient.

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User Authentication by Gait Data from Smartphone Sensors Using Hybrid Deep Learning Network

Qian Cao, +2 more
- 29 Jun 2022 - 
TL;DR: This work proposed a Hybrid Deep Learning Network (HDLN), which combined the advantages of a long short-term memory (LSTM) network and a convolutional neural network to reliably extract discriminative features from complex smartphone inertial data and developed an APP that can achieve real-time gait recognition.
Journal ArticleDOI

Amalgamation of stress wave force balance with artificial intelligence: An alternative way of drag force measurement in supersonic flows

TL;DR: In this article , the performance of the Adaptive Neuro Fuzzy Inference System (ANFIS) and de-convolution algorithm was evaluated in an impulse test facility.
References
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Journal ArticleDOI

Measurement of Angular Acceleration of a Rigid Body Using Linear Accelerometers

TL;DR: In this article, it was shown that the combination resonances appear to involve the differences, rather than the sums, of the low frequencies involved in a cantilevered pipe conveying fluid.
Journal ArticleDOI

Hypersonic aerothermodynamic and scramjet research using high enthalpy shock tunnel

TL;DR: The largest high enthalpy shock tunnel HIEST was built at NAL Kakuda in 1997, aiming for aerodynamic tests of Japan's space vehicle HOPE and scramjet propulsion systems as discussed by the authors.
Journal ArticleDOI

Experiments on supersonic combustion ramjet propulsion in a shock tunnel

TL;DR: In this paper, the axial force on the model was measured using a stress wave force balance, which is a recently developed technique for measuring forces in shock tunnels, and the results were found to compare satisfactorily with predictions based on established theoretical models, used with simplifying approximations.
Journal ArticleDOI

Improvement of a free piston driver for a high-enthalpy shock tunnel

TL;DR: In this article, the authors proposed a simple piston/gas-dynamic model, and the tuned operation condition was formulated as an eigenvalue with which the piston has sufficiently high speed at the moment of diaphragm rupture, so as to maintain a constant driver gas pressure, and reduces its speed to come to rest when very closely approaching the end of the driver tube.
Journal ArticleDOI

Millisecond aerodynamic force measurement with side-jet model in theISL shock tunnel

TL;DR: In this paper, the authors present a measurement of the aerodynamic force produced by interaction of side jets and ambient flow and act on the surface of the model, and quantify the time necessary to establish quasistationary flow for the actual test conditions.
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